Coordinated beamforming in cellular and cognitive radio networks

Abstract This thesis focuses on the design of coordinated downlink beamforming techniques for wireless multi-cell multi-user multi-antenna systems. In particular, cellular and cognitive radio networks are considered. In general, coordinated beamforming schemes aim to improve system performance, especially at the cell-edge area, by controlling inter-cell interference. In this work, special emphasis is put on practical coordinated beamforming designs that can be implemented in a decentralized manner by relying on local channel state information (CSI) and low-rate backhaul signaling. The network design objective is the sum power minimization (SPMin) of base stations (BSs) while providing the guaranteed minimum rate for each user. Decentralized coordinated beamforming techniques are developed for cellular multi-user multiple-input single-output (MISO) systems. The proposed iterative algorithms are based on classical primal and dual decomposition methods. The SPMin problem is decomposed into two optimization levels, i.e., BS-specific subproblems for the beamforming design and a network-wide master problem for the inter-cell interference coordination. After the acquisition of local CSI, each BS can independently compute its transmit beamformers by solving the subproblem via standard convex optimization techniques. Interference coordination is managed by solving the master problem via a traditional subgradient method that requires scalar information exchange between the BSs. The algorithms make it possible to satisfy the user-specific rate constraints for any iteration. Hence, delay and signaling overhead can be reduced by limiting the number of performed iterations. In this respect, the proposed algorithms are applicable to practical implementations unlike most of the existing decentralized approaches. The numerical results demonstrate that the algorithms provide significant performance gains over zero-forcing beamforming strategies. Coordinated beamforming is also studied in cellular multi-user multiple-input multiple-output (MIMO) systems. The corresponding non-convex SPMin problem is divided into transmit and receive beamforming optimization steps that are alternately solved via successive convex approximation method and the linear minimum mean square error criterion, respectively, until the desired level of convergence is attained. In addition to centralized design, two decentralized primal decomposition-based algorithms are proposed wherein the transmit and receive beamforming designs are facilitated by a combination of pilot and backhaul signaling. The results show that the proposed MIMO algorithms notably outperform the MISO ones. Finally, cellular coordinated beamforming strategies are extended to multi-user MISO cognitive radio systems, where primary and secondary networks share the same spectrum. Here, network optimization is performed for the secondary system with additional interference constraints imposed for the primary users. Decentralized algorithms are proposed based on primal decomposition and an alternating direction method of multipliers.Tiivistelmä Tämä väitöskirja keskittyy yhteistoiminnallisten keilanmuodostustekniikoiden suunnitteluun langattomissa monisolu- ja moniantennijärjestelmissä, erityisesti solukko- ja kognitiiviradioverkoissa. Yhteistoiminnalliset keilanmuodostustekniikat pyrkivät parantamaan verkkojen suorituskykyä kontrolloimalla monisoluhäiriötä, erityisesti tukiasemasolujen reuna-alueilla. Tässä työssä painotetaan erityisesti käytännöllisten yhteistoiminnallisten keilanmuodostustekniikoiden suunnittelua, joka voidaan toteuttaa hajautetusti perustuen paikalliseen kanavatietoon ja tukiasemien väliseen informaationvaihtoon. Verkon suunnittelutavoite on minimoida tukiasemien kokonaislähetysteho samalla, kun jokaiselle käyttäjälle taataan tietty vähimmäistiedonsiirtonopeus. Hajautettuja yhteistoiminnallisia keilanmuodostustekniikoita kehitetään moni-tulo yksi-lähtö -solukkoverkoille. Oletuksena on, että tukiasemat ovat varustettuja monilla lähetysantenneilla, kun taas päätelaitteissa on vain yksi vastaanotinantenni. Ehdotetut iteratiiviset algoritmit perustuvat klassisiin primaali- ja duaalihajotelmiin. Lähetystehon minimointiongelma hajotetaan kahteen optimointitasoon: tukiasemakohtaisiin aliongelmiin keilanmuodostusta varten ja verkkotason pääongelmaan monisoluhäiriön hallintaa varten. Paikallisen kanavatiedon hankkimisen jälkeen jokainen tukiasema laskee itsenäisesti lähetyskeilansa ratkaisemalla aliongelmansa käyttäen apunaan standardeja konveksioptimointitekniikoita. Monisoluhäiriötä kontrolloidaan ratkaisemalla pääongelma käyttäen perinteistä aligradienttimenetelmää. Tämä vaatii tukiasemien välistä informaationvaihtoa. Ehdotetut algoritmit takaavat käyttäjäkohtaiset tiedonsiirtonopeustavoitteet jokaisella iterointikierroksella. Tämä mahdollistaa viiveen pienentämisen ja tukiasemien välisen informaatiovaihdon kontrolloimisen. Tästä syystä ehdotetut algoritmit soveltuvat käytännön toteutuksiin toisin kuin useimmat aiemmin ehdotetut hajautetut algoritmit. Numeeriset tulokset osoittavat, että väitöskirjassa ehdotetut algoritmit tuovat merkittävää verkon suorituskyvyn parannusta verrattaessa aiempiin nollaanpakotus -menetelmiin. Yhteistoiminnallista keilanmuodostusta tutkitaan myös moni-tulo moni-lähtö -solukkoverkoissa, joissa tukiasemat sekä päätelaitteet ovat varustettuja monilla antenneilla. Tällaisessa verkossa lähetystehon minimointiongelma on ei-konveksi. Optimointiongelma jaetaan lähetys- ja vastaanottokeilanmuodostukseen, jotka toistetaan vuorotellen, kunnes algoritmi konvergoituu. Lähetyskeilanmuodostusongelma ratkaistaan peräkkäisillä konvekseilla approksimaatioilla. Vastaanottimen keilanmuodostus toteutetaan summaneliövirheen minimoinnin kautta. Keskitetyn algoritmin lisäksi tässä työssä kehitetään myös kaksi hajautettua algoritmia, jotka perustuvat primaalihajotelmaan. Hajautettua toteutusta helpotetaan pilottisignaloinnilla ja tukiasemien välisellä informaationvaihdolla. Numeeriset tulokset osoittavat, että moni-tulo moni-lähtö -tekniikoilla on merkittävästi parempi suorituskyky kuin moni-tulo yksi-lähtö -tekniikoilla. Lopuksi yhteistoiminnallista keilanmuodostusta tarkastellaan kognitiiviradioverkoissa, joissa primaari- ja sekundaarijärjestelmät jakavat saman taajuuskaistan. Lähetystehon optimointi suoritetaan sekundaariverkolle samalla minimoiden primaarikäyttäjille aiheuttamaa häiriötä. Väitöskirjassa kehitetään kaksi hajautettua algoritmia, joista toinen perustuu primaalihajotelmaan ja toinen kerrointen vaihtelevan suunnan menetelmään

Transmission strategies for throughput maximization in high speed train communications:from theoretical study to practical algorithms

Abstract This paper focuses on improving the downlink throughput of the base station (BS)-to-train communication link in a high-speed train (HST) scenario. First, we provide a theoretical study of the throughput maximization problem in a single-cell multiple-input-multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) train scenario with and without cooperation among carriages. The aim is to give fundamental insight into the problem rather than providing practically realizable algorithms. The theoretical study suggests that it is highly advantageous to exploit the size of the train by increasing the number of antennas and further allowing the carriages to cooperate. In the practical system-level study, we propose two low-complexity MIMO-OFDM transmission schemes, which are based on simple antenna selection (AS) methods with spatial multiplexing. The main idea is to select the best transmit antennas among different antenna combinations by comparing their estimated throughput performances. The simulation results show that the proposed algorithms outperform Long-Term Evolution (LTE)-based dynamic rank transmission schemes in terms of throughput and computational load in practical HST scenarios. Unlike the exhaustive search type of dynamic transmission schemes, our simple algorithms are also applicable to large antenna arrays. In conclusion, large antenna arrays with simple AS and spatial multiplexing transmission strategies seem to be potential solutions to the significant improvement of the throughput of the BS-to-train link in HST scenarios

Multi-cell interference coordination for multigroup multicast transmission

Abstract Multicasting has become a particularly important technique in the context of cache-enabled cloud radio access networks proposed for 5G systems, where it can be used to transmit common information to multiple users to improve both spectral and energy efficiency. For the efficient spectrum utilization, the future communications are based on aggressive frequency reuse, where the required data rates can be achieved with multiple-input multiple-output precoding techniques. This approach, however, calls for advanced interference coordination techniques. This paper summarizes some of the core approaches proposed in the literature and discusses the main future challenges

Distributed optimization for coordinated beamforming in multicell multigroup multicast systems:power minimization and SINR balancing

Abstract This paper considers coordinated multicast beamforming in a multicell multigroup multiple-input single-output system. Each base station (BS) serves multiple groups of users by forming a single beam with common information per group. We propose centralized and distributed beamforming algorithms for two different optimization targets. The first objective is to minimize the total transmission power of all the BSs while guaranteeing the user-specific minimum quality-of-service targets. The semidefinite relaxation (SDR) method is used to approximate the nonconvex multicast problem as a semidefinite program (SDP), which is solvable via centralized processing. Subsequently, two alternative distributed methods are proposed. The first approach turns the SDP into a two-level optimization via primal decomposition. At the higher level, intercell interference powers are optimized for fixed beamformers, whereas the lower level locally optimizes the beamformers by minimizing BS-specific transmit powers for the given intercell interference constraints. The second distributed solution is enabled via an alternating direction method of multipliers, where the intercell interference optimization is divided into a local and a global optimization by forcing the equality via consistency constraints. We further propose a centralized and a simple distributed beamforming design for the signal-to-interference-plus-noise ratio (SINR) balancing problem in which the minimum SINR among the users is maximized with given per-BS power constraints. This problem is solved via the bisection method as a series of SDP feasibility problems. The simulation results show the superiority of the proposed coordinated beamforming algorithms over traditional noncoordinated transmission schemes, and illustrate the fast convergence of the distributed methods

Hybrid beamforming for single-user MIMO with partially connected RF architecture

Abstract Hybrid analog-digital beamforming has been recognized as a promising solution for a practical implementation of massive multiple-input multiple-output (MIMO) systems based on millimeter-wave technology. In this paper, three hybrid beamforming algorithms are proposed for single-user MIMO systems with partially connected radio frequency (RF) architecture, including a singular value decomposition (SVD) matching algorithm, an iterative orthogonalization algorithm, and a transmit-receive zero forcing (ZF) algorithm. The rate performance of the proposed algorithms is compared with fully digital and analog beamforming in a realistic geometry-based stochastic channel model. The simulation results show that the transmit-receive ZF is superior among hybrid methods, and it provides performance relatively close to that of the digital beamforming. In conclusion, carefully designed partially connected hybrid beamforming can obtain an excellent balance between hardware complexity and performance

Partially connected hybrid beamforming for large antenna arrays in multi-user MISO systems

Abstract Hybrid beamforming (HBF) is a promising approach to be employed in millimeter-wave massive MIMO systems. In this paper, four HBF algorithms with partially connected radio frequency architecture are proposed for large antenna arrays in multi-user MISO systems. The first two algorithms aim at minimizing the difference between either the fully digital zero forcing (ZF) or maximum ratio transmission (MRT) beamformer, and the hybrid beamformer of each user. The other two algorithms apply either ZF or MRT HBF solution to each subarray. The average sum rate performance of the proposed algorithms are evaluated using a realistic geometry-based stochastic channel model, and compared with digital ZF and MRT approaches. Numerical results demonstrate that the subarray-based ZF algorithm is superior to other proposed hybrid methods in all simulation cases

Rate maximization for partially connected hybrid beamforming in single-user MIMO systems

Abstract Partially connected hybrid beamforming (HBF) is a promising approach to alleviate the implementation of large scale millimeter-wave multiple-input multiple-output (MIMO) systems. In this paper, we develop rate maximizing algorithms for the full array-and subarray-based processing strategies of partially connected HBF. We formulate the rate maximization problem as a weighted mean square error minimization problem and use alternating optimization to tackle it. Numerical results show that partially connected HBF provides a good balance between hardware complexity and performance in comparison to optimal fully digital and analog beamforming. Moreover, the simpler subarray-based HBF algorithm achieves comparable performance to that of the full array-based approach in medium and high SNRs. The rate maximizing results serve as upper bounds for lower complexity heuristic methods

Energy-efficient multicell multigroup multicasting with joint beamforming and antenna selection

Abstract This paper studies the energy efficiency and sum rate tradeoff for coordinated beamforming in multicell multiuser multigroup multicast multiple-input single-output systems. We first consider a conventional network energy efficiency maximization (EEmax) problem by jointly optimizing the transmit beamformers and antennas selected to be used in transmission. We also account for per-antenna maximum power constraints to avoid nonlinear distortion in power amplifiers and user-specific minimum rate constraints to guarantee certain service levels and fairness. To be energy efficient, transmit antenna selection is employed. It eventually leads to a mixed-Boolean fractional program. We then propose two different approaches to solve this difficult problem. The first solution is based on a novel modeling technique that produces a tight continuous relaxation. The second approach is based on sparsity-inducing method, which does not require the introduction of any Boolean variable. We also investigate the tradeoff between the energy efficiency and sum rate by proposing two different formulations. In the first formulation, we propose a new metric, that is, the ratio of the sum rate and the so-called weighted power. Specifically, this metric reduces to EEmax when the weight is 1, and to sum rate maximization when the weight is 0. In the other method, we treat the tradeoff problem as a multiobjective optimization for which a scalarization approach is adopted. Numerical results illustrate significant achievable energy efficiency gains over the method where the antenna selection is not employed. The effect of antenna selection on the energy efficiency and sum rate tradeoff is also demonstrated

4everPack: Final report

4everPack was a two year Business Finland research project aimed at generating understanding on packaging reuse in fast moving consumer goods (FMCG).The project observed reuse from multiple perspectives• Packaging materials (WP1)• Packaging monitoring and traceability (WP2)• Reuse logistics (WP3)• European consumer acceptance (WP4)• Circular business models (WP5The project consortia included VTT, University of Vaasa and 14 companies